Assessing community resilience: mapping the community rating system (CRS) against the 6C-4R frameworks
Bibliographic record
Abstract
This paper introduces an holistic approach to assessing community resilience in the United States with respect to hazards by inventorying a community&s;s strengths: Financial, Human, Natural, Physical, Political and Social, as sources of capital (6 Capitals, or 6Cs) and characterizing four properties of its resilience (4R) (robustness, resourcefulness, redundancy and rapidity). We link the 6C-4R framework to the National Flood Insurance Program&s;s (NFIP) Community Rating System (CRS). There is a positive correlation between the 6C-4R framework and the CRS, demonstrating the extent to which that system might therefore be used to measure resilience holistically in an effective and efficient manner. We also provide illustrative examples of resilience strategies linked to the 6C-4R framework that were adopted by Ottawa, Illinois, Birmingham, Alabama and Cedar Rapids, Iowa, USA, the last being a community that joined the CRS in 2010 following a severe flood in 2008. The CRS does not cover all the aspects of a community&s;s status and activities so in order to make informed decisions and prioritize the implementation of resilience-improving activities, community-wide cost–benefit analyses of CRS activities would be useful in the future as inputs for further developing a strategy for reducing future flood losses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".